Papers
9
Total Citations
158
H-Index
5
About
Didier Coquin is a researcher whose work sits at the intersection of computer vision, human-robot interaction, and intelligent information fusion. His primary research areas include gesture and posture recognition, multi-sensor data fusion, and fuzzy logic systems, with a strong focus on enabling more intuitive human-robot communication. Coquin’s most influential contribution is his early work on dynamic hand gesture recognition using skeletal data (2005, 96 citations), which laid foundational methods for interpreting human gestures in virtual and robotic environments. He has since advanced the field by integrating fuzzy systems and evidence theory—most notably in color recognition tasks for the NAO humanoid robot—demonstrating how uncertain sensor data can be robustly fused for real-world applications. His more recent exploration of thick fuzzy sets (2020) offers a novel framework for handling uncertainty in computational modeling. Coquin’s work has practical implications for assistive robotics, IoT-enabled vision systems, and example-based teaching for robots, making him a notable figure in applied intelligent systems research.
Research Focus
Key Achievements
Top Papers
- 1Dynamic Hand Gesture Recognition Using the Skeleton of the Hand96 citations · 2005
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- 5Color Recognition for NAO Robot Using Sugeno Fuzzy System and Evidence5 citations · 2015
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- 8Body posture measurement in a context of example-based teaching2 citations · 2015
- 9Decision Method Choice in a Human Posture Recognition Context2 citations · 2017